Prediction of SSE Shanghai Enterprises index based on bidirectional LSTM model of air pollutants

作者：

Highlights：

• A new stock price prediction model is proposed (BiLSTM).

• The BiLSTM model is constructed based on air pollutant data.

• BiLSTM model can achieve effective stock price prediction.

• BiLSTM model has certain applicability.

• The experimental data set is replicable.

摘要

•A new stock price prediction model is proposed (BiLSTM).•The BiLSTM model is constructed based on air pollutant data.•BiLSTM model can achieve effective stock price prediction.•BiLSTM model has certain applicability.•The experimental data set is replicable.

论文关键词：Stock market forecast,Deep learning,Long short-term memory (LSTM),Air quality index

论文评审过程：Received 17 April 2021, Revised 9 May 2022, Accepted 13 May 2022, Available online 19 May 2022, Version of Record 25 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117600